Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

Irina N. Dolganova; Nikita V. Chernomyrdin; Polina V. Aleksandrova; Sheyh-Islyam T. Beshplav; Aleksandr A. Potapov M.D.; Igor V. Reshetov; Vladimir N. Kurlov; Valery V. Tuchin; Kirill I. Zaytsev

doi:10.1117/1.JBO.23.9.091406

11 April 2018 Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

Irina N. Dolganova, Nikita V. Chernomyrdin, Polina V. Aleksandrova, Sheyh-Islyam T. Beshplav, Aleksandr A. Potapov M.D., Igor V. Reshetov, Vladimir N. Kurlov, Valery V. Tuchin, Kirill I. Zaytsev

Author Affiliations +

Journal of Biomedical Optics, Vol. 23, Issue 9, 091406 (April 2018). https://doi.org/10.1117/1.JBO.23.9.091406

Abstract

We present the nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography (OCT). It employs an experimental training algorithm based on imaging of a test-object, made of the colloidal suspension of the monodisperse nanoparticles and contains the microscale inclusions. The geometry and the scattering properties of the test-object are known a priori allowing us to set the criteria for the training algorithm. Using a wide set of the wavelet kernels and the wavelet-domain filtration approaches, the appropriate filter is constructed based on the test-object imaging. We apply the proposed approach and chose an efficient wavelet denoising procedure by considering the combinations of the decomposition basis from five wavelet families with eight types of the filtration threshold. We demonstrate applicability of the wavelet-filtering for the in vitro OCT image of human brain meningioma. The observed results prove high efficiency of the proposed OCT image denoising technique.

1. Introduction

Since optical coherence tomography (OCT) was introduced by Huang et al.,¹ it has become an effective noninvasive imaging modality mainly in biomedical applications.²^,³ Its effectiveness was shown in ophthalmology⁴^–⁸ (especially for diagnostics of retina diseases⁵^,⁷^–¹⁰), neuroscience,¹¹^–¹⁵ vascular and blood imaging,¹⁶^,¹⁷ dermatology,¹⁸^–²⁰ etc. Recent developments in OCT technology show its potential for studying prostate²¹ and reproductive organs pathologies,²² and malignant tissues detection.¹³^,²³^–²⁶

OCT uses the principles of low-coherence interferometry either in time-domain or in frequency-domain.²⁷^–³² Doppler and polarization-sensitive OCT-techniques allow for specific functional and structural tissue imaging.³³ Ongoing development of hardware, such as fiber optics, light sources, mechanical and optical components, used in OCT systems constantly improve image resolution. At the same time, the reliability of OCT measurements can be significantly increased by designing of adequate algorithms for solution of the OCT inverse ill-posed problems.³⁴^–³⁶ However, speckle and scattering noise of the OCT signal prevents the data reconstruction;³⁷^–⁴⁰ thus, finding an appropriate denoising technique remains one of the challenging problems of OCT technologies.

Nowadays, two general approaches of noise reduction in OCT images are available. The first one is applied during the OCT image acquisition and is based on improvement of the detector integration time or averaging of several scans. The second one involves postprocessing techniques, including filtering, methods of extraction of “pure” signal, and deconvolution procedures.⁴¹ Using more than one OCT scans for noise reduction has a significant drawback for in vivo imaging, when object movements become critical. In this case, the averaging should include algorithms of image alignment. Making some assumptions about noise statistics and properties, such methods as independent component analysis,⁴² robust principal component analysis,⁴³ and statistical-based approach⁴⁴ give appropriate results, but remain computationally complicated and time-consuming.

Digital filtering as an effective method of noise reduction has been recently implemented in various ways.⁹^,¹⁰^,⁴⁵^–⁵¹ Since it requires a single OCT image, it can be applied for in vivo and in vitro biomedical imaging. Median filtration is a common, simple, and quite effective method.⁹^,⁴⁷^,⁴⁸ The histogram-based threshold filters show better filtration quality but require knowledge of a priori information about the object.⁴⁶ For objects with repeatedly appearing features, it is possible to use a nonlocal mean filter with double Gaussian anisotropic kernels,¹⁰ which yields better results in comparison to Wiener, median, and bilateral filters. Effectively used in image formation and processing, wavelet analysis was applied in OCT for noise reduction.⁴⁹^–⁵² Wavelet kernels and optical wave packets demonstrate similar physical features: zero means value, finite energy corresponding to square norm one, and high locality in both time and frequency domains. The wavelet analysis seems to be optimal for the decomposition of time-domain optical data and, in particular, for the denoising of OCT images.⁵³ Wavelet analysis employs different types of kernels and filtration methods, thus, choosing the most effective filter parameters for OCT imaging of the particular object of interest remains a challenging problem.

In this paper, we propose the nanoparticle-enabled experimentally trained wavelet-domain denoising technique for OCT. We suggest using a test-object for experimental training, which is made of monodisperse nanoparticle suspension and contains microscale inclusions. To set the criteria for the experimental training of the wavelet-domain denoising procedure, we use information about the geometry and the scattering properties of the test-object, which are known a priori and could be tuned with a high precision by changing the nanoparticle size, suspension concentration, geometry and positions of the microscale inclusions. By imaging of the test-object and implementing the wavelet denoising to experimental processing, we choose the appropriate combination of the wavelet decomposition basis and the filtration threshold. We present the results of the experimental demonstration of the proposed technique. In order to show the prospectives of the developed technique for biomedical applications of OCT, we apply it for in vitro imaging of human brain meningioma, which seems to be a representative object for the proposed denoising technique. It features a morphologically heterogeneous structure and often contains tumorous cell clusters (whorls) and collagen balls,¹³ which could be treated as local microscale inclusions in a semi-infinite scattering space. The observed results demonstrate an ability for denoising of the meningioma OCT images while sustaining all the structural features of biotissues. This justifies high efficiency of the proposed OCT denoising procedure and prospectives of its use in noninvasive and intraoperative medical diagnosis.

2. Nanoparticle-Based Test-Object

We prepare the test-object [see Fig. 1(a)] for the experimental training of the wavelet-domain filter. First, we make a glass cuvette for the test-object, where we place three $80 - μ m$ -diameter rigidly fixed cylindrical Cu-wires serving as the microscale inclusions. These wires are placed at the depth of 0.3 mm from the cover window of the cuvette and spaced at the fixed distances of $r_{1} = 0.5 mm$ and $r_{2} = 0.4 mm$ measured by visible microscopy. Second, we make aqueous colloidal suspension, i.e., spherical monodisperse nanoparticles of amorphous silica in water. In order to make uniform particle concentration within the suspension volume, we apply ultrasound mixer. We use nanoparticles with diameter of about 400 nm, synthesized by the multistage Stober method.⁵⁴^,⁵⁵ Third, we fill the cuvette with the prepared colloidal suspension. During the described preparation of the test-object, we can precisely control its geometrical parameters and scattering properties. These values can be tuned by changing the suspension concentration or/and the positions of the wires. Using the test-object, we can physically simulate OCT imaging of the semi-infinite scattering media with the backscattering inclusions, which is one of the most common cases for many OCT applications.¹³

Fig. 1

(a) OCT imaging of the test-object: a scheme of the test-object; (b)–(e) OCT images of the test-object for different concentrations of colloidal suspension of monodisperse nanoparticles $C_{0} / 16$ , $C_{0} / 4$ , $C_{0} / 2$ , and $C_{0}$ , respectively.

The quantitative criterion for training of the denoising technique could be based on the prior knowledge of the test-object, for instance, the distances between the reflecting inclusions. In our research, we evaluate an efficiency of the noise reduction by estimating, from the denoised images, the relative distance $r_{1} / r_{2}$ between the wires and comparing it with the actual value ${(r_{1} / r_{2})}_{0} = 1.25$ . We perform imaging of the test-object using the OCT system OCT1300Y (Institute of Applied Physics RAS, Nizny Novgorod, Russia), described in detail in Refs. 35, 56, and 57. The experimental setup operates in the near-infrared range and employs laser radiation with the central wavelength of $1.3 μ m$ and the average power of 0.75 mW. It yields A- and B-scans of the sample and produces the $256 \times 400 pixel$ images with the 4-s acquisition time. The declared resolution of the OCT system is $50 μ m$ in lateral (A-scan) and $30 μ m$ (B-scan) in depth directions (in the air).

For the experimental training, we prepared four different concentrations of the colloidal suspension: $C = C_{0} / 16$ , $C_{0} / 4$ , $C_{0} / 2$ , and $C_{0}$ , where $C_{0} ≃ 1 %$ is the maximal suspension concentration. Such values were used to demonstrate applicability of the proposed denoising approach in a wide range of scattering parameters, while for the particular object of interest, the scattering parameters should be adjusted precisely as close to the object as possible. OCT imaging of the test-object with these properties yields the results demonstrated in Fig. 1, i.e., the distortions of the OCT images are quite obvious. In particular, strong light scattering in highly concentrated suspension is the reason for distortions and increased background intensity in the image from Fig. 1(e). Moreover, the “wakes,” appearing around the metal inclusions in images from Figs. 1(d) and 1(e), can strongly impact the accuracy of estimating the object geometry. At the same time, for the smallest suspension concentration [Fig. 1(b)], the metal inclusions are represented with the sharp intensity peaks. Such distortion of the OCT images complicates analysis and processing of the OCT-imaging data and justifies the importance of introduction of an efficient denoising procedure. These “wakes” might appear as a result of combination of light reflection from metal objects and multiple scattering in the surrounding colloidal suspension. In the case of biological tissues, such artifacts are not expected, and other factors will distort the signal like absorption, surface roughness, or bulk scattering. In general, an effective denoising procedure should suppress these factors and retain information about the object, i.e., interface position, scattering coefficient profile, or position and sizes of the inclusions. For demonstration, we used symmetrical metal objects, assuming that filtration should retain or reconstruct their initial symmetry and thus, the position of center of mass. Depending on the main extracted key features, we could select different criteria and test samples, including ones, accounting for the asymmetric geometry of the sample. For instance, one could use transparent inclusions of spherical or plane form made of glass or plastic.

3. Principle of the Wavelet-Domain Denoising

After registration of OCT images of the test-object, the wavelet filtration of the experimental data is performed. At this stage, various combinations of the wavelet bases and the wavelet-domain filtration procedures are examined in order to select the optimal one.

OCT signal has a form of a spatial distribution of the scattered light intensity $I (x, z)$ , where $z$ is a sample depth and $x$ is a lateral coordinate. Wavelet-domain denoising could be separately applied to different cross-sections $x = x^{'}$ of this two-dimensional field, namely, to $I_{x} (z) = I (x = x^{'}, z)$ . This procedure includes three main steps.

• Step 1. Direct wavelet transformation:
Eq. (1)
$C (a, b) = W [I (z)] = \int_{- \infty}^{+ \infty} I_{x} (z) ψ (a, b, z) d z,$
where $ψ (z)$ is the mother wavelet and $ψ (a, b, z)$ are the wavelet-decomposition kernels:
Eq. (2)
$ψ (a, b, z) = {| a |}^{- 1 / 2} ψ (\frac{z - b}{a}),$
for which $a$ and $b$ define the scale and the translation, respectively.
• Step 2. Wavelet-domain thresholding:
Eq. (3)
$C_{T} (a, b) = {\begin{cases} C (a, b), & if C (a, b) \geq T, \\ 0, & if C (a, b) < T, \end{cases}$
where $T$ is the threshold value.
• Step 3. Inverse wavelet transformation:
Eq. (4)
$I_{x}^{'} (z) = W^{- 1} [C_{T} (a, b)] = C_{ψ}^{- 1} \iint_{- \infty}^{+ \infty} \frac{C_{T} (a, b)}{a^{2}} \tilde{ψ} (a, b, z) d a d b,$
where $\tilde{ψ} (a, b, z)$ is the dual function of $ψ (a, b, z)$ , and
Eq. (5)
$C_{ψ} = \int_{- \infty}^{+ \infty} \frac{Ψ (ω) \tilde{Ψ} (ω)}{| ω |} d ω < \infty$
is the admissible constant restricting the diversity of functions suitable for the definition of the mother wavelet $ψ (z)$ . Functions $Ψ (ω)$ and $\tilde{Ψ} (ω)$ in Eq. (5) correspond to the Fourier spectra of $ψ (z)$ and $\tilde{ψ} (z)$ , respectively.

We implement methods of the fast direct and inverse wavelet transformations (FDWT and FIWT)⁵⁸ to force the computations of Eqs. (1) and (4). We consider various wavelet bases from the ones used in FDWT and FIWT algorithms, both “soft” and “hard” thresholding modalities,⁵⁹^,⁶⁰ and different number of the decomposition levels $L$ . Each configuration of the wavelet-domain filter is applied to the initial images [see Figs. 1(b)–1(e)], then, using the image thresholding procedure, we determine three image segments with high pixel intensity and find their centroids⁶¹ (i.e., centers of mass). Finally, we estimate the corresponding relative distance for the filtered image ${(r_{1} / r_{2})}_{F}$ . This value is compared with the reference one ${(r_{1} / r_{2})}_{0}$ :

Eq. (6)

d_{i} = | {(r_{1} / r_{2})}_{F} - {(r_{1} / r_{2})}_{0} |, i = 1 \dots N,

for every

i

’th of

N

experiment iterations with the similar object configuration, each of which is characterized by various realization of equal-magnitude noise. The standard deviation (STD) of this estimation is then determined:

Eq. (7)

σ_{STD} = {(\frac{1}{N} \sum_{i = 1}^{N} d_{i}^{2})}^{1 / 2} .

By comparing the $σ_{STD}$ values based on $N = 5$ measurements, we find the minimal one and the related appropriate wavelet-domain filter configuration. The described experimentally trained wavelet-domain denoising algorithm is summarized in Fig. 2.

Fig. 2

A scheme of the nanoparticle-enabled experimentally trained wavelet-domain denoising technique, which uses the accuracy of estimating the relative distance between the microscale inclusions $r_{1} / r_{2}$ as a criterion of the noise suppression efficiency.

4. Choosing the Wavelet-Domain Filter and Application for OCT Imaging

For the described test-object and the corresponding OCT images (Fig. 1), we apply the proposed algorithm. Our database includes 52 mother wavelets of five wavelet families widely applied in FDWT and FIWT algorithms, i.e., daubechies (db), coiflets (coif), symlets (sym), biorthogonal (bior), and reverse biorthogonal (rbio). We use soft (s) and hard (h) thresholding procedures and four values of $L$ .

Figure 3 shows an error map presented in logarithmic scale $\log (1 + σ_{STD})$ for different suspension concentrations $C$ . The colormap in this figure is changed from red to dark blue with an increase of the error. By analyzing all values, we selected seven cases, which are marked with blue ovals and correspond to the minimal error below 0.01 from the observed ones. As it is clear from Fig. 3, there is no unique filter configuration with the equally good results for all of the sample concentrations. We could notice that the measurement errors do not depend monotonically on scatterer concentration, which could be due to nonmonotonical changes of the scattering coefficient of turbid media with increased volume fraction of colloidal particles,⁶²^,⁶³ as well as due to complex dependence of the Mie scattering cross-section of microinclusions on the effective refractive index of colloidal suspension.³¹ Moreover, when small concentrations of nanoparticles produce weak noise, $σ_{STD}$ has insignificant variations and all considered filter realizations have the similar effect, in contrast to the high concentration with large $σ_{STD}$ variations.

Fig. 3

Wavelet-domain filter selection: (a)–(d) colored diagrams represent errors of ${(r_{1} / r_{2})}_{f}$ estimation produced by denoising procedure for the colloidal suspension concentration of $C_{0} / 16$ , $C_{0} / 4$ , $C_{0} / 2$ , and $C_{0}$ , respectively. Horizontal lines in each panel correspond to eight (1) to (8) different thresholding methods, i.e., soft (s) and hard (h) thresholding procedures and decomposition level $L$ . Vertical lines correspond to 52 mother wavelets of five wavelet families. Seven combinations of filter parameters with lowest errors are marked with dark blue ovals.

Figure 4 compares seven selected cases of the wavelet-domain filtration. It highlights the concentration influence. When all cases of the sample condition have to be considered, the averaging can help find the appropriate filter parameters. The inset in Fig. 4 demonstrates the mean value ${⟨ [\log (1 + σ_{STD})] ⟩}_{C}$ . Among these results, we can find that the sixth case bior3.5(4) has better performance for our OCT system and sample. Depending on the goal of OCT imaging, one could increase the number of noise conditions and change the scattering medium.

Fig. 4

Analysis of the wavelet-domain denoising performance: (a) a comparison of errors, produced by the seven selected filter parameters and (b) a mean value of error for different suspension concentrations $C$ .

In order to prove our results, we compare the filtered images for the optimal wavelet filter configuration bior3.5(4) with nonoptimal ones (Fig. 5). Configuration sym8(8) corresponds to the highest error among the seven selected cases (Fig. 4); this filter [Figs. 5(i)–5(l)] demonstrates satisfactory speckle reduction but adds dark artifacts and allocates particles’ centers at high concentrations less distinctly [Figs. 5(k) and 5(l)] than configuration bior3.5(4). Configuration bior3.3(5) corresponds to the highest error value among all results from our database. Its application [Figs. 5(m)–5(p)] distorts the initial image, adds dark regions and deformations of the sample internal structure. Therefore, bior3.5(4) operates well for both low and high concentrations and confirms the obtained results.

Fig. 5

(a)–(d) Results of wavelet filtration of the initial images (e)–(h) applying the optimal filter parameters, (i)–(l) filtration with one of the seven selected filters with high error, and (m)–(p) the non-optimal filtration with the highest error.

5. Applications of the Denoising Procedure for OCT-Imaging of Meningioma

The latest research demonstrates that OCT could become an effective intraoperative imaging technique for neurosurgery, where fast and accurate detection of tumor margins is of high importance.¹⁴^,²³^,⁶⁴^,⁶⁵ Recently, numerous modern techniques of tissue imaging are considered to solve this problem: intraoperative magnetic resonance imaging,⁶⁶^,⁶⁷ terahertz reflectometry,⁶⁸^–⁷⁰ Raman spectroscopy,⁷¹^–⁷³ and fluorescence imaging.⁷⁴^,⁷⁵ Among them, OCT remains one of the most promising instrument, which yields noninvasive, fast, and label-free 2-D and 3-D visualisation of tissues, providing both lateral and depth information about its structure and scattering properties.

Many biomedical applications of OCT imaging deal with objects featuring finally structured heterogeneous semi-infinite scattering medium with local inclusions possessing high (or low) scattering. Representative examples of this type of objects are tissues of retina,⁷ cortex,¹² liver,²⁴ embryo,²² and brain.¹³ For example, brain meningiomas are characterized by a nonhomogeneous internal structure owing to the presence of cell clusters (whorls) and collagen balls.¹³ For the listed tissues, the proposed denoising technique would yield improvement of the OCT image quality, i.e., suppressing the noises and the scattering background, while sustaining and even emphasizing the inclusions, which could serve as specific features of tumorous tissues. Therefore, in order to highlight the potential of the proposed denoising technique for OCT imaging of tissues, we apply the selected wavelet-filter bior3.5(4) for processing the OCT image of the in vitro human brain meningioma (Fig. 6).

Fig. 6

Application of the wavelet denoising procedure with bior3.5(4) filter for OCT image of human brain tumor (meningioma): (a) initial and (b) filtered B-scan of the (c) brain meningioma tissue sample; (d) and (f) magnified regions of the B-scan before and (e) and (g) after filtration, respectively; (h)–(k) A-scans before (orange lines) and after (black lines) filtration.

The sample of brain meningioma tissue [Fig. 6(c)] is explored no later than 4 h after its resection, performed in Burdenko Neurosurgery Institute. To fix the tissue, prevent its hydration/dehydration, and sustain its structure and composition during both transportation and OCT imaging, we place the sample on a reference optically transparent substrate and cover it with gelatin films.⁷⁶ The examined tissue sample is excised according to the initial medical diagnosis. After the OCT imaging, it is fixed in formalin and sent to the histological examination, which is aimed to confirm the result of preoperative diagnosis.

The initial OCT image and the denoising data are demonstrated in Figs. 6(a) and 6(b). This example of brain tumorous tissue contains several inclusions of different scale. The large-scale inhomogeneities are quite visible from the initial OCT scan, while scattering noise prevents observation of the small-scale ones. Two regions in green rectangles shown on panels [Figs. 6(d)–6(g)] demonstrate an effective denoising inside small circle-like inclusions. This makes the OCT image more sufficient for further segmentation and processing. Moreover, we analyze four vertical cross-sections of the OCT image, marked on both the initial and filtered images. These results are demonstrated in Figs. 6(h)–6(k). Initial intensity of OCT A-scans is shown in red color, and filtered ones are shown in black. It is visible from these panels that the application of the selected wavelet filter leads to noise suppression, while the position and form of all inhomogeneities of tissue are sustained.

6. Discussions

The proposed denoising method could be useful for various OCT applications in biology, medicine, and material science. Despite that we have considered only a representative example of the wavelet-domain denoising, the applied type of nanoparticle test-object can be changed for enabling a better solution for other special denoising problems. The proposed criterion could be effective for training the wavelet-domain filter in such a common case, when we need better detection of microscale inclusions and their relative positions. If other type of data needs to be analyzed from the OCT image, the criterion can be modified according to this purpose. Accordingly, different wavelet filters can become optimal for the same OCT image depending on the extracted information from the object. For example, the experimentally trained wavelet-domain denoising approach could be applied to study the multilayered systems or to measure the scattering parameters of media by solving the OCT inverse scattering problems. However, an appropriate wavelet filter should be constructed to accommodate the needs of the particular OCT application. In further studies, this method would be used for in vivo measurements of living tissues, and the test-sample parameters would be corrected significantly.

Furthermore, this method could be generalized for various applications of signal processing, assuming not only the OCT data but also other types of optical and even acoustic signals. Further implementations of the experimentally trained wavelet denoising technique can appear for terahertz pulsed spectroscopy⁵³^,⁷⁷^,⁷⁸ and time-of-flight tomography,⁷⁹ and frequency-resolved optical gating.⁸⁰ Moreover, the proposed approach has significant potential for autocorrelation methods⁸¹ applied to measure the ultra-short optical pulses, ultrasonic measurements in biology, medicine, nondesctructive testing,⁸²^,⁸³ magnetic resonance microscopy,⁸⁴ etc.

The described method relies on the wavelet analysis, but, in general case, techniques such as fractional Fourier transform and chirplet analysis can be used for optimizing the image decomposition onto a basis of kernels local in both time/space domain and frequency domain. This improvement will lead to accounting for other specific properties of analyzed data, for example, complex internal structure or spectral properties.

The proposed approach of manufacturing the test-object based on colloidal suspension of nanoparticles seems to be useful for solution of the inverse scattering problems in optics, where various methods can be experimentally trained using the prepared test-objects. For example, colloid suspensions of various nanoparticles (amorphous silica, polymers), their mixtures, magnetic materials, and even photonic crystals and amorphous media based on self-assembling from colloid suspension⁸⁵ could be applied to simulate, in a simple manner, the media with spatially inhomogeneous scattering properties.

7. Conclusions

In this work, we proposed the nanoparticle-enabled experimentally trained wavelet-domain denoising technique for OCT. The used test-object was manufactured from the aqueous suspension of the colloidal monodisperse nanoparticles and contained the microscale inclusions made of Cu wires. The geometrical and scattering properties of the test-object were determined a priori and were used for setting the criteria and adjusting conditions of the training algorithm. The proposed test-object and criterion can be changed according to the stated denoising problem. We used a wide database of wavelet filter parameters for training and determined the most satisfactory filter configuration for the particular OCT measurement conditions. In order to highlight the prospectives of the proposed denoising approach in biology and medicine, we applied it for the in vitro imaging of the heterogeneous tissues—human brain meningiomas. The observed results demonstrated high efficiency of the proposed denoising technique. This general approach could be used for a wide range of applications of biomedical optics and biophotonics.

Disclosures

The authors have no relevant financial interests in this article and no potential conflicts of interest to disclose.

Acknowledgments

Data processing (I.N.D. and N.V.C.) was supported by the Russian Science Foundation (RSF), Project # 14-19-01083. Gelatin embedding of biotissues (S.T.B. and K.I.Z.) was supported by the Russian Science Foundation (RSF), Project # 17-79-20346. Experimental studies (V.V.T.) were supported by the RFBR [17-00-00275 (17-00-00272)] and Government of Russian Federation (074-U01, project 5-100).

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Biography

Irina N. Dolganova received her PhD in engineering sciences. Currently, he is a researcher in the Institute of Solid State Physics of Russian Academy of Sciences, Chernogolovka, Russia (ISSP RAS) and a researcher and an associated professor at Bauman Moscow State Technical University (BMSTU), Moscow, Russia. Her research interests include optics and biophotonics, light scattering, optical coherence tomography, and denoising of optical signals.

Nikita V. Chernomyrdin is currently a researcher at A.M. Prokhorov General Physics Institute of Russian Academy of Sciences, Moscow, Russia (GPI RAS) and a researcher and an assistant professor at BMSTU Moscow, Russia. His research interests include biomedical optics, applications of terahertz technologies for medical diagnosis, and high-resolution terahertz imaging systems.

Polina V. Aleksandrova is currently a student at BMSTU Moscow, Russia. Her research interests include optical coherence tomography, biomedical optics, and OCT denoising methods.

Sheykh-Islyam T. Beshplav is currently a clinical resident of Neurooncology Department at Burdenko Neurosurgery Institute. His research interests include corpus callosum, brain tumors, 5-ALA fluorescence, terahertz biomedicine, and optical coherence tomography.

Alexander A. Potapov is a professor of neurosurgery and a director of Burdenko Neurosurgery Institute, Moscow. His research interests include brain tumors, brain trauma, fluorescence, and spectroscopy.

Igor V. Reshetov is a doctor of medical sciences, a corresponding member of the Russian Academy of Sciences. Currently, he is a professor at I.M. Sechenov First Moscow State Medical University, Moscow, Russia (FMSMU) and a practicing surgeon. His research interests include plastic and reconstructive microsurgery in oncology, head and neck tumors, including technologies for treatment, reconstruction, rehabilitation, and development of novel technologies for medical diagnosis of human body malignancies.

Vladimir N. Kurlov received his Dr. Sci. degree in engineering sciences from the National University of Science and Technology MISIS, Moscow, Russia, in 2004. He is the head in the Shaped Crystal Growth Laboratory at the Institute of Solid State Physics of Russian Academy of Sciences (ISSP RAS), Chernogolovka, Russia. His research interests include crystallography, bulk and shaped crystal growth technologies, including, sapphire and other oxide crystals, and applications of crystals in optics and biomedical sciences.

Valery V. Tuchin is a professor and head of optics and biophotonics at Saratov State University (National Research University of Russia) and several other universities. His research interests include tissue optics, laser medicine, tissue optical clearing, and nanobiophotonics. He is a fellow of SPIE and OSA. He has been awarded Honored Science Worker of the Russia, Honored Professor of Saratov University, SPIE Educator Award, FiDiPro, Finland, Chime Bell Prize of Hubei Province, China, NanQiang Life Science Series Lectures Award of Xiamen University, China, and the Joseph W. Goodman Book Writing Award (OSA/SPIE).

Kirill I. Zaytsev received his PhD in engineering sciences. Currently, he is a head in the laboratory at A.M. Prokhorov General Physics Institute of Russian Academy of Sciences, Moscow, Russia (GPI RAS) and a researcher and an associated professor at BMSTU Moscow, Russia. His research interests include optics and biophotonics, inverse problems in optics, dielectric spectroscopy, terahertz science and technology, and computational electrodynamics.

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Irina N. Dolganova, Nikita V. Chernomyrdin, Polina V. Aleksandrova, Sheyh-Islyam T. Beshplav, Aleksandr A. Potapov M.D., Igor V. Reshetov, Vladimir N. Kurlov, Valery V. Tuchin, and Kirill I. Zaytsev "Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography," Journal of Biomedical Optics 23(9), 091406 (11 April 2018). https://doi.org/10.1117/1.JBO.23.9.091406

Received: 5 January 2018; Accepted: 16 March 2018; Published: 11 April 2018

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KEYWORDS

Optical coherence tomography

Denoising

Wavelets

Image filtering

Scattering

Tissues

Nanoparticles

1.

Introduction

2.

Nanoparticle-Based Test-Object

Fig. 1

3.

Principle of the Wavelet-Domain Denoising

Eq. (1)

Eq. (2)

Eq. (3)

Eq. (4)

Eq. (5)

Eq. (6)

Eq. (7)

Fig. 2

4.

Choosing the Wavelet-Domain Filter and Application for OCT Imaging

Fig. 3

Fig. 4

Fig. 5

5.

Applications of the Denoising Procedure for OCT-Imaging of Meningioma

Fig. 6

6.

Discussions

7.

Conclusions

Disclosures

Acknowledgments

References

Biography

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